In general, underwater images suffer from color distortion and low contrast,
because light is attenuated and backscattered as it propagates through water
(differently depending on wavelength and on the properties of the water body).
An existing simple degradation model (similar to atmospheric image "hazing"
effects), though helpful, is not sufficient to properly represent the
underwater image degradation because there are unaccounted for and
non-measurable factors e.g. scattering of light due to turbidity of water,
reflective characteristics of turbid medium etc. We propose a deep
learning-based architecture to automatically simulate the underwater effects
where only a dehazing-like image formation equation is known to the network,
and the additional degradation due to the other unknown factors if inferred in
a data-driven way. We only use RGB images (because in real-time scenario depth
image is not available) to estimate the depth image. For testing, we have
proposed (due to the lack of real underwater image datasets) a complex image
formation model/equation to manually generate images that resemble real
underwater images (used as ground truth). However, only the classical image
formation equation (the one used for image dehazing) is informed to the
network. This mimics the fact that in a real scenario, the physics are never
completely known and only simplified models are known. Thanks to the ground
truth, generated by a complex image formation equation, we could successfully
perform a qualitative and quantitative evaluation of proposed technique,
compared to other purely data driven approaches

通过采用基于深度学习的架构，我们能够自动模拟水下图像的效果，通过数据驱动的方式推断其他未知因素导致的降级现象，以估计只有 RGB 图像可用的深度图像。通过与其他纯数据驱动方法的比较，我们通过复杂的图像生成模型成功地对该技术进行了定性和定量评估。

基于物理和数据驱动的水下图像模拟与残差学习

Physics Informed and Data Driven Simulation of Underwater Images via  Residual Learning

In recent years, with the continuous development of the marine industry,
underwater image enhancement has attracted plenty of attention. Unfortunately,
the propagation of light in water will be absorbed by water bodies and
scattered by suspended particles, resulting in color deviation and low
contrast. To solve these two problems, we propose an Adaptive Transmission and
Dynamic Color guided network (named ATDCnet) for underwater image enhancement.
In particular, to exploit the knowledge of physics, we design an Adaptive
Transmission-directed Module (ATM) to better guide the network. To deal with
the color deviation problem, we design a Dynamic Color-guided Module (DCM) to
post-process the enhanced image color. Further, we design an
Encoder-Decoder-based Compensation (EDC) structure with attention and a
multi-stage feature fusion mechanism to perform color restoration and contrast
enhancement simultaneously. Extensive experiments demonstrate the
state-of-the-art performance of the ATDCnet on multiple benchmark datasets.

最近几年随着海洋产业的不断发展，水下图像增强引起了广泛关注。为了解决颜色偏差和低对比度的问题，我们提出了一种自适应透射和动态颜色引导网络（命名为 ATDCnet）用于水下图像增强。

水下图像增强的传输和颜色引导网络

Transmission and Color-guided Network for Underwater Image Enhancement

Underwater object detection (UOD) plays a significant role in aquaculture and
marine environmental protection. Considering the challenges posed by low
contrast and low-light conditions in underwater environments, several
underwater image enhancement (UIE) methods have been proposed to improve the
quality of underwater images. However, only using the enhanced images does not
improve the performance of UOD, since it may unavoidably remove or alter
critical patterns and details of underwater objects. In contrast, we believe
that exploring the complementary information from the two domains is beneficial
for UOD. The raw image preserves the natural characteristics of the scene and
texture information of the objects, while the enhanced image improves the
visibility of underwater objects. Based on this perspective, we propose a Gated
Cross-domain Collaborative Network (GCC-Net) to address the challenges of poor
visibility and low contrast in underwater environments, which comprises three
dedicated components. Firstly, a real-time UIE method is employed to generate
enhanced images, which can improve the visibility of objects in low-contrast
areas. Secondly, a cross-domain feature interaction module is introduced to
facilitate the interaction and mine complementary information between raw and
enhanced image features. Thirdly, to prevent the contamination of unreliable
generated results, a gated feature fusion module is proposed to adaptively
control the fusion ratio of cross-domain information. Our method presents a new
UOD paradigm from the perspective of cross-domain information interaction and
fusion. Experimental results demonstrate that the proposed GCC-Net achieves
state-of-the-art performance on four underwater datasets.

提出了 GCC-Net 方法，该方法通过融合纯净图像和增强后的图像，利用跨域合作的特征交互模块来改进水下物体检测的可见性。实验结果表明该方法在四个水下数据集上取得了最先进的性能。